





import argparse
import pathlib

import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("""1.0.0a"""):
      raise Exception("""requires fairseq >= 1.0.0a""")

logging.set_verbosity_info()
snake_case								=							logging.get_logger(__name__)

snake_case								=							"""Hello world! cécé herlolip"""

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:str      ,				snake_case__	:str      ,				snake_case__	:bool  )   ->    Any:
  _lowercase						    =			FairseqRobertaModel.from_pretrained(snake_case__  )
  roberta.eval()  # disable dropout
  _lowercase						    =			roberta.model.encoder.sentence_encoder
  _lowercase						    =			XLMRobertaConfig(
      vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings      ,				hidden_size=roberta.cfg.model.encoder_embed_dim      ,				num_hidden_layers=roberta.cfg.model.encoder_layers      ,				num_attention_heads=roberta.cfg.model.encoder_attention_heads      ,				intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim      ,				max_position_embeddings=514      ,				type_vocab_size=1      ,				layer_norm_eps=1E-5      ,				)
  if classification_head:
    _lowercase						    =			roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]

  print('Our RoBERTa config:'      ,				snake_case__  )

  _lowercase						    =			XLMRobertaXLForSequenceClassification(snake_case__  ) if classification_head else XLMRobertaXLForMaskedLM(snake_case__  )
  model.eval()

  # Now let's copy all the weights.
  # Embeddings
  _lowercase						    =			roberta_sent_encoder.embed_tokens.weight
  _lowercase						    =			roberta_sent_encoder.embed_positions.weight
  _lowercase						    =			torch.zeros_like(
      model.roberta.embeddings.token_type_embeddings.weight  )  # just zero them out b/c RoBERTa doesn't use them.

  _lowercase						    =			roberta_sent_encoder.layer_norm.weight
  _lowercase						    =			roberta_sent_encoder.layer_norm.bias

  for i in range(config.num_hidden_layers  ):
    # Encoder: start of layer
    _lowercase						    =			model.roberta.encoder.layer[i]
    _lowercase						    =			roberta_sent_encoder.layers[i]

    _lowercase						    =			layer.attention
    _lowercase						    =			roberta_layer.self_attn_layer_norm.weight
    _lowercase						    =			roberta_layer.self_attn_layer_norm.bias

    # self attention
    _lowercase						    =			layer.attention.self
    assert (
        roberta_layer.self_attn.k_proj.weight.data.shape
        == roberta_layer.self_attn.q_proj.weight.data.shape
        == roberta_layer.self_attn.v_proj.weight.data.shape
        == torch.Size((config.hidden_size, config.hidden_size)  )
    )

    _lowercase						    =			roberta_layer.self_attn.q_proj.weight
    _lowercase						    =			roberta_layer.self_attn.q_proj.bias
    _lowercase						    =			roberta_layer.self_attn.k_proj.weight
    _lowercase						    =			roberta_layer.self_attn.k_proj.bias
    _lowercase						    =			roberta_layer.self_attn.v_proj.weight
    _lowercase						    =			roberta_layer.self_attn.v_proj.bias

    # self-attention output
    _lowercase						    =			layer.attention.output
    assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
    _lowercase						    =			roberta_layer.self_attn.out_proj.weight
    _lowercase						    =			roberta_layer.self_attn.out_proj.bias

    # this one is final layer norm
    _lowercase						    =			roberta_layer.final_layer_norm.weight
    _lowercase						    =			roberta_layer.final_layer_norm.bias

    # intermediate
    _lowercase						    =			layer.intermediate
    assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
    _lowercase						    =			roberta_layer.fca.weight
    _lowercase						    =			roberta_layer.fca.bias

    # output
    _lowercase						    =			layer.output
    assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
    _lowercase						    =			roberta_layer.fca.weight
    _lowercase						    =			roberta_layer.fca.bias
    # end of layer

  if classification_head:
    _lowercase						    =			roberta.model.classification_heads['mnli'].dense.weight
    _lowercase						    =			roberta.model.classification_heads['mnli'].dense.bias
    _lowercase						    =			roberta.model.classification_heads['mnli'].out_proj.weight
    _lowercase						    =			roberta.model.classification_heads['mnli'].out_proj.bias
  else:
    # LM Head
    _lowercase						    =			roberta.model.encoder.lm_head.dense.weight
    _lowercase						    =			roberta.model.encoder.lm_head.dense.bias
    _lowercase						    =			roberta.model.encoder.lm_head.layer_norm.weight
    _lowercase						    =			roberta.model.encoder.lm_head.layer_norm.bias
    _lowercase						    =			roberta.model.encoder.lm_head.weight
    _lowercase						    =			roberta.model.encoder.lm_head.bias

  # Let's check that we get the same results.
  _lowercase						    =			roberta.encode(snake_case__  ).unsqueeze(0  )  # batch of size 1

  _lowercase						    =			model(snake_case__  )[0]
  if classification_head:
    _lowercase						    =			roberta.model.classification_heads['mnli'](roberta.extract_features(snake_case__  )  )
  else:
    _lowercase						    =			roberta.model(snake_case__  )[0]
  print(our_output.shape      ,				their_output.shape  )
  _lowercase						    =			torch.max(torch.abs(our_output - their_output  )  ).item()
  print(F"""max_absolute_diff = {max_absolute_diff}"""  )  # ~ 1e-7
  _lowercase						    =			torch.allclose(snake_case__      ,				snake_case__      ,				atol=1E-3  )
  print('Do both models output the same tensors?'      ,				'🔥' if success else '💩'  )
  if not success:
    raise Exception('Something went wRoNg'  )

  pathlib.Path(snake_case__  ).mkdir(parents=snake_case__      ,				exist_ok=snake_case__  )
  print(F"""Saving model to {pytorch_dump_folder_path}"""  )
  model.save_pretrained(snake_case__  )


if __name__ == "__main__":
      snake_case								=							argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--roberta_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
      )
      parser.add_argument(
          """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
      )
      parser.add_argument(
          """--classification_head""", action="""store_true""", help="""Whether to convert a final classification head."""
      )
      snake_case								=							parser.parse_args()
      convert_xlm_roberta_xl_checkpoint_to_pytorch(
          args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
      )







"""simple docstring"""





def 	lowerCamelCase__			(				_lowerCamelCase			:						str					) ->							bool:
  lowerCamelCase_       	=		0
  for ch in input_str:
    lowerCamelCase_       	=		ord(_lowerCamelCase					)
    lowerCamelCase_       	=		pow(2						,							_lowerCamelCase					)

    # If we already turned on bit for current character's unicode
    if bitmap >> ch_unicode & 1 == 1:
      return False
    bitmap |= ch_bit_index_on
  return True


if __name__ == "__main__":
  import doctest

  doctest.testmod()




